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用合成市场轨迹训练深度Q网络进行交易

文章 arXiv papers · 作者: Thibaut Théate et al.
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总结

论文介绍交易深度Q网络（TDQN），这是一种深度强化学习方法，用于随时间选择股票市场头寸。该方法将DQN应用于交易，并将夏普比率设为策略力求最大化的绩效指标。其目标是为交易期间找到合适的头寸，而非直接套用未经调整的通用强化学习算法。

智能体使用由有限历史股票数据生成的人工轨迹进行训练。论文还提出一种更严格的交易策略评估方法，并报告称，按该方法评估时，TDQN结果颇有前景。所提供的说明没有具体指出市场、数据集细节、基准策略、数值结果或评估流程，因此仅凭本文无法判断所报告的表现或其普适性。合成轨迹和有限的历史数据是该方法的核心特征，但文中没有解释其构造方式及影响。

核心观点

  • TDQN将深度Q学习应用于选择股票市场交易头寸。
  • 该策略旨在最大化夏普比率。
  • 训练使用由有限历史数据生成的人工轨迹。
  • 论文提出一种更严格的绩效评估方法，并报告称结果颇有前景。
  • 现有说明没有提供评估稳健性所需的实现细节和数值证据。

标签

股票机器学习统计学回测风险管理
全文
# An Application of Deep Reinforcement Learning to Algorithmic Trading


# An Application of Deep Reinforcement Learning to Algorithmic Trading









This scientific research paper presents an innovative approach based on deep reinforcement learning (DRL) to solve the algorithmic trading problem of determining the optimal trading position at any point in time during a trading activity in stock markets. It proposes a novel DRL trading strategy so as to maximise the resulting Sharpe ratio performance indicator on a broad range of stock markets. Denominated the Trading Deep Q-Network algorithm (TDQN), this new trading strategy is inspired from the popular DQN algorithm and significantly adapted to the specific algorithmic trading problem at hand. The training of the resulting reinforcement learning (RL) agent is entirely based on the generation of artificial trajectories from a limited set of stock market historical data. In order to objectively assess the performance of trading strategies, the research paper also proposes a novel, more rigorous performance assessment methodology. Following this new performance assessment approach, promising results are reported for the TDQN strategy.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。